





import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__A							:			str         =			logging.get_logger(__name__)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			['input_values', 'attention_mask']




     def __init__(    self				,     snake_case_ = 1				,     snake_case_ = 1_6000				,     snake_case_ = 0.0				,     snake_case_ = False				,     snake_case_ = 80				,     snake_case_ = 16				,     snake_case_ = 64				,     snake_case_ = "hann_window"				,     snake_case_ = 1.0				,     snake_case_ = 80				,     snake_case_ = 7600				,     snake_case_ = 1E-10				,     snake_case_ = 2				,     snake_case_ = True				,     **snake_case_				,     ):
         super().__init__(feature_size=snake_case_				,     sampling_rate=snake_case_				,     padding_value=snake_case_				,     **snake_case_							)
         _A     =							do_normalize
         _A     =							return_attention_mask

         _A     =							num_mel_bins
         _A     =							hop_length
         _A     =							win_length
         _A     =							win_function
         _A     =							frame_signal_scale
         _A     =							fmin
         _A     =							fmax
         _A     =							mel_floor
         _A     =							reduction_factor

         _A     =							win_length * sampling_rate // 1000
         _A     =							hop_length * sampling_rate // 1000
         _A     =							optimal_fft_length(self.sample_size							)
         _A     =							(self.n_fft // 2) + 1

         _A     =							window_function(window_length=self.sample_size				,     name=self.win_function				,     periodic=snake_case_							)

         _A     =							mel_filter_bank(
             num_frequency_bins=self.n_freqs				,     num_mel_filters=self.num_mel_bins				,     min_frequency=self.fmin				,     max_frequency=self.fmax				,     sampling_rate=self.sampling_rate				,     norm='slaney'				,     mel_scale='slaney'				,     )

         if frame_signal_scale != 1.0:
             warnings.warn(
                 'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'				,     snake_case_				,     )
         if reduction_factor != 2.0:
             warnings.warn(
                 'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'				,     snake_case_				,     )




     @staticmethod
     # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
     def        lowerCAmelCase__		(    snake_case_				,     snake_case_				,     snake_case_ = 0.0							):
         if attention_mask is not None:
             _A     =							np.array(snake_case_				,     np.intaa							)
             _A     =							[]

             for vector, length in zip(snake_case_				,     attention_mask.sum(-1							)							):
                 _A     =							(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7							)
                 if length < normed_slice.shape[0]:
                     _A     =							padding_value

                 normed_input_values.append(snake_case_							)
         else:
             _A     =							[(x - x.mean()) / np.sqrt(x.var() + 1E-7							) for x in input_values]

         return normed_input_values




     def        lowerCAmelCase__		(    self				,     snake_case_				,     ):
         _A     =							spectrogram(
             snake_case_				,     window=self.window				,     frame_length=self.sample_size				,     hop_length=self.sample_stride				,     fft_length=self.n_fft				,     mel_filters=self.mel_filters				,     mel_floor=self.mel_floor				,     log_mel='log10'				,     )
         return log_mel_spec.T




     def __call__(    self				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     **snake_case_				,     ):
         if audio is None and audio_target is None:
             raise ValueError('You must provide either `audio` or `audio_target` values.'							)

         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     F"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
                     F" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
                     F" {self.sampling_rate} and not {sampling_rate}."							)
         else:
             logger.warning(
                 'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
                 'Failing to do so can result in silent errors that might be hard to debug.'							)

         if audio is not None:
             _A     =							self._process_audio(
                 snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     **snake_case_				,     )
         else:
             _A     =							None

         if audio_target is not None:
             _A     =							self._process_audio(
                 snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     **snake_case_				,     )

             if inputs is None:
                 return inputs_target
             else:
                 _A     =							inputs_target['input_values']
                 _A     =							inputs_target.get('attention_mask'							)
                 if decoder_attention_mask is not None:
                     _A     =							decoder_attention_mask

         return inputs




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = False				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     **snake_case_				,     ):
         _A     =							isinstance(snake_case_				,     np.ndarray							) and len(speech.shape							) > 1
         if is_batched_numpy and len(speech.shape							) > 2:
             raise ValueError(F"Only mono-channel audio is supported for input to {self}"							)
         _A     =							is_batched_numpy or (
             isinstance(snake_case_				,     (list, tuple)							) and (isinstance(speech[0]				,     (np.ndarray, tuple, list)							))
         )

         if is_batched:
             _A     =							[np.asarray(snake_case_				,     dtype=np.floataa							) for speech in speech]
         elif not is_batched and not isinstance(snake_case_				,     np.ndarray							):
             _A     =							np.asarray(snake_case_				,     dtype=np.floataa							)
         elif isinstance(snake_case_				,     np.ndarray							) and speech.dtype is np.dtype(np.floataa							):
             _A     =							speech.astype(np.floataa							)

         # always return batch
         if not is_batched:
             _A     =							[speech]

         # needed to make pad() work on spectrogram inputs
         _A     =							self.feature_size

         # convert into correct format for padding
         if is_target:
             _A     =							[self._extract_mel_features(snake_case_							) for waveform in speech]
             _A     =							BatchFeature({'input_values': features}							)
             _A     =							self.num_mel_bins
         else:
             _A     =							BatchFeature({'input_values': speech}							)

         _A     =							self.pad(
             snake_case_				,     padding=snake_case_				,     max_length=snake_case_				,     truncation=snake_case_				,     pad_to_multiple_of=snake_case_				,     return_attention_mask=snake_case_				,     **snake_case_				,     )

         _A     =							feature_size_hack

         # convert input values to correct format
         _A     =							padded_inputs['input_values']
         if not isinstance(input_values[0]				,     np.ndarray							):
             _A     =							[np.asarray(snake_case_				,     dtype=np.floataa							) for array in input_values]
         elif (
             not isinstance(snake_case_				,     np.ndarray							)
             and isinstance(input_values[0]				,     np.ndarray							)
             and input_values[0].dtype is np.dtype(np.floataa							)
         ):
             _A     =							[array.astype(np.floataa							) for array in input_values]
         elif isinstance(snake_case_				,     np.ndarray							) and input_values.dtype is np.dtype(np.floataa							):
             _A     =							input_values.astype(np.floataa							)

         # convert attention_mask to correct format
         _A     =							padded_inputs.get('attention_mask'							)
         if attention_mask is not None:
             _A     =							[np.asarray(snake_case_				,     dtype=np.intaa							) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if not is_target and self.do_normalize:
             _A     =							(
                 attention_mask
                 if self._get_padding_strategies(snake_case_				,     max_length=snake_case_							) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             _A     =							self.zero_mean_unit_var_norm(
                 padded_inputs['input_values']				,     attention_mask=snake_case_				,     padding_value=self.padding_value							)

         if return_tensors is not None:
             _A     =							padded_inputs.convert_to_tensors(snake_case_							)

         return padded_inputs




     def        lowerCAmelCase__		(    self							):
         _A     =							super().to_dict()

         # Don't serialize these as they are derived from the other properties.
         _A     =							['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
         for name in names:
             if name in output:
                 del output[name]

         return output











'''simple docstring'''







import socket







def 						_A      ( ):






   '''simple docstring'''







   A__       =     socket.socket(socket.AF_INET      ,socket.SOCK_STREAM						)
   A__       =     socket.gethostname()
   A__       =     12312

   sock.connect((host, port)						)
   sock.send(B'Hello server!'						)

   with open('Received_file'      ,'wb'						) as out_file:
      print('File opened'						)
      print('Receiving data...'						)
      while True:
         A__       =     sock.recv(1024						)
         if not data:
            break
         out_file.write(UpperCAmelCase						)

   print('Successfully received the file'						)
   sock.close()
   print('Connection closed'						)


if __name__ == "__main__":
    main()


